Cross Sensor Iris and Periocular Database

50 Participants

10 Mobile Setups

2000+ Images

+ Ground-Truth

When attempting to perform iris or periocular biometrics on mobile environments, several problems arise: 1) the wide variety of camera sensors and lenses mobile phones and tablets come equipped with produce discrepancies in working images, as they are acquired with color distortions, at multiple resolutions, etc.; 2) on-the-go acquisition by potentially untrained subjects will result in demanding Pose, Illumination and Expression (PIE) changes, as not all users hold their mobile devices at the same position, resulting in varying acquisition angles and scales, or rotated images; the acquisition environment can have poor or insufficient lighting, and uncontrolled outdoor daylight will most likely produce spectacle reflections over the iris region; 3) etc.

The main objective of the CSIP database was to gather images from a representative group of participants, acquired over cross-sensor setups and varying acquisition scenarios, thus mimicking the conditions faced on mobile application scenarios. Along with the data acquired with different mobile devices, an iris segmentation mask is also provided, allowing assessing the performance of both iris and periocular segmentation and recognition algorithms on mobile environments.

Considering the heterogeneity of camera sensor/lens setups consumer mobile devices can deliver, a total of 10 different setups were used during the dataset acquisition stage: four different devices, some of them with frontal and rear cameras and LED flash. Aiming at mimicking the variability of noise factors associated with on-the-go recognition, participants were not imaged at a single particular location, but on multiple sites, as they were, with artificial, natural and mixed illumination conditions. As so, there is a substantial difference between each acquisition setup and surrounding conditions, even when the same setup was used to capture images from different subjects. From visual inspection, eight different noise factors are distinguishable, and can affect the biometric recognition process: multiple scales; chromatic distortions; image rotation; poor lighting; off-angle acquisition; out-of-focus images; deviated gaze; and iris obstructions (including reflexions).

For each periocular image acquired by the mobile devices, a binary iris segmentation mask is provided with the CSIP dataset. Those masks were automatically obtained using a state-of-the-art iris segmentation approach particularly suitable for uncontrolled acquisition conditions, which has been corroborated by the first place achieved at the Noisy Iris Challenge Evaluation - Part 1 (NICE.I)1.